Bird, O;
Galiza, EP;
Baxter, DN;
Boffito, M;
Browne, D;
Burns, F;
Chadwick, DR;
... Ster, IC; + view all
(2024)
The predictive role of symptoms in COVID-19
diagnostic models: A longitudinal insight.
Epidemiology and Infection
, 152
, Article e37. 10.1017/S0950268824000037.
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Abstract
To investigate the symptoms of SARS-CoV-2 infection, their dynamics and their discriminatory power for the disease using longitudinally, prospectively collected information reported at the time of their occurrence. We have analysed data from a large phase 3 clinical UK COVID-19 vaccine trial. The alpha variant was the predominant strain. Participants were assessed for SARS-CoV-2 infection via nasal/throat PCR at recruitment, vaccination appointments, and when symptomatic. Statistical techniques were implemented to infer estimates representative of the UK population, accounting for multiple symptomatic episodes associated with one individual. An optimal diagnostic model for SARS-CoV-2 infection was derived. The 4-month prevalence of SARS-CoV-2 was 2.1%; increasing to 19.4% (16.0%-22.7%) in participants reporting loss of appetite and 31.9% (27.1%-36.8%) in those with anosmia/ageusia. The model identified anosmia and/or ageusia, fever, congestion, and cough to be significantly associated with SARS-CoV-2 infection. Symptoms' dynamics were vastly different in the two groups; after a slow start peaking later and lasting longer in PCR+ participants, whilst exhibiting a consistent decline in PCR- participants, with, on average, fewer than 3 days of symptoms reported. Anosmia/ageusia peaked late in confirmed SARS-CoV-2 infection (day 12), indicating a low discrimination power for early disease diagnosis.
| Type: | Article |
|---|---|
| Title: | The predictive role of symptoms in COVID-19 diagnostic models: A longitudinal insight |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1017/S0950268824000037 |
| Publisher version: | http://dx.doi.org/10.1017/s0950268824000037 |
| Language: | English |
| Additional information: | Creative Common License - CCCreative Common License - BY This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. Copyright © The Author(s), 2024. Published by Cambridge University Press |
| Keywords: | coronavirus, longitudinal data, symptoms dynamics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology > MRC Clinical Trials Unit at UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health > Infection and Population Health |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10188740 |
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